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   "source": [
    "# Finding Critical Points using RNNs\n",
    "\n",
    " - [Data Prep notebook](https://www.kaggle.com/code/werus23/sleep-critical-point-prepare-data)\n",
    " - [Training notebook](https://www.kaggle.com/code/werus23/sleep-critical-point-train/)\n",
    " - Inference Notebook - This\n",
    "\n",
    "Credits:\n",
    "\n",
    " - idea: https://www.kaggle.com/competitions/child-mind-institute-detect-sleep-states/discussion/441470\n",
    " - dataloader: https://www.kaggle.com/code/henriupton/efficient-loading-memory-usage-visualizations-cmi\n",
    " - arch: https://www.kaggle.com/competitions/tlvmc-parkinsons-freezing-gait-prediction/discussion/416410"
   ]
  },
  {
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   "id": "46d47373",
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    {
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     "output_type": "stream",
     "text": [
      "/home/wcx/.venvs/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import gc\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm.auto import tqdm \n",
    "\n",
    "from math import pi, sqrt, exp\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import Dataset\n",
    "plt.style.use(\"ggplot\")\n",
    "\n",
    "from pyarrow.parquet import ParquetFile\n",
    "import pyarrow as pa \n",
    "torch.set_num_interop_threads(4)\n",
    "torch.set_num_threads(4)\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
   ]
  },
  {
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   "execution_count": 3,
   "id": "dea3593c",
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   "outputs": [],
   "source": [
    "class PATHS:\n",
    "    MAIN_DIR = \"../data/\"\n",
    "    # CSV FILES : \n",
    "    SUBMISSION = MAIN_DIR + \"sample_submission.csv\"\n",
    "    TRAIN_EVENTS = MAIN_DIR + \"train_events.csv\"\n",
    "    # PARQUET FILES:\n",
    "    TRAIN_SERIES = MAIN_DIR + \"train_series.parquet\"\n",
    "    TEST_SERIES = MAIN_DIR + \"test_series.parquet\"\n",
    "    TEST_SERIES_1 = \"./test_series_1.parquet\"\n",
    "class CFG:\n",
    "    DEMO_MODE = True\n",
    "class data_reader:\n",
    "    def __init__(self, demo_mode):\n",
    "        super().__init__()\n",
    "        # MAPPING FOR DATA LOADING :\n",
    "        self.names_mapping = {\n",
    "            \"submission\" : {\"path\" : PATHS.SUBMISSION, \"is_parquet\" : False, \"has_timestamp\" : False}, \n",
    "            \"train_events\" : {\"path\" : PATHS.TRAIN_EVENTS, \"is_parquet\" : False, \"has_timestamp\" : True},\n",
    "            \"train_series\" : {\"path\" : PATHS.TRAIN_SERIES, \"is_parquet\" : True, \"has_timestamp\" : True},\n",
    "            \"test_series\" : {\"path\" : PATHS.TEST_SERIES, \"is_parquet\" : True, \"has_timestamp\" : True},\n",
    "            \"test_series_1\" : {\"path\" : PATHS.TEST_SERIES_1, \"is_parquet\" : True, \"has_timestamp\" : True}\n",
    "        }\n",
    "        self.valid_names = [\"submission\", \"train_events\", \"train_series\", \"test_series\"]\n",
    "        self.demo_mode = demo_mode\n",
    "    \n",
    "    def verify(self, data_name):\n",
    "        \"function for data name verification\"\n",
    "        if data_name not in self.valid_names:\n",
    "            print(\"PLEASE ENTER A VALID DATASET NAME, VALID NAMES ARE : \", data_name)\n",
    "        return\n",
    "    \n",
    "    def cleaning(self, data):\n",
    "        \"cleaning function : drop na values\"\n",
    "        before_cleaning = len(data)\n",
    "        print(\"Number of missing timestamps : \", len(data[data[\"timestamp\"].isna()]))\n",
    "        data = data.dropna(subset=[\"timestamp\"])\n",
    "        after_cleaning = len(data)\n",
    "        print(\"Percentage of removed rows : {:.1f}%\".format(100 * (before_cleaning - after_cleaning) / before_cleaning) )\n",
    "        return data\n",
    "    \n",
    "    @staticmethod\n",
    "    def reduce_memory_usage(data):\n",
    "        \"iterate through all the columns of a dataframe and modify the data type to reduce memory usage.\"\n",
    "        start_mem = data.memory_usage().sum() / 1024**2\n",
    "        print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n",
    "        for col in data.columns:\n",
    "            col_type = data[col].dtype    \n",
    "            if col_type != object:\n",
    "                c_min = data[col].min()\n",
    "                c_max = data[col].max()\n",
    "                if str(col_type)[:3] == 'int':\n",
    "                    if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                        data[col] = data[col].astype(np.int8)\n",
    "                    elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                        data[col] = data[col].astype(np.int16)\n",
    "                    elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                        data[col] = data[col].astype(np.int32)\n",
    "                    elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                        data[col] = data[col].astype(np.int64)  \n",
    "                else:\n",
    "                    if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                        data[col] = data[col].astype(np.float16)\n",
    "                    elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                        data[col] = data[col].astype(np.float32)\n",
    "                    else:\n",
    "                        data[col] = data[col].astype(np.float64)\n",
    "            else:\n",
    "                data[col] = data[col].astype('category')\n",
    "\n",
    "        end_mem = data.memory_usage().sum() / 1024**2\n",
    "        print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n",
    "        print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n",
    "        return data\n",
    "    \n",
    "    def load_data(self, data_name):\n",
    "        \"function for data loading\"\n",
    "        self.verify(data_name)\n",
    "        data_props = self.names_mapping[data_name]\n",
    "        if data_props[\"is_parquet\"]:\n",
    "            if self.demo_mode:\n",
    "                pf = ParquetFile(data_props[\"path\"]) \n",
    "                demo_rows = next(pf.iter_batches(batch_size=20_000)) \n",
    "                data = pa.Table.from_batches([demo_rows]).to_pandas()\n",
    "            else:\n",
    "                data = pd.read_parquet(data_props[\"path\"])\n",
    "        else:\n",
    "            if self.demo_mode:\n",
    "                data = pd.read_csv(data_props[\"path\"], nrows=20_000)\n",
    "            else:\n",
    "                data = pd.read_csv(data_props[\"path\"])\n",
    "                \n",
    "        gc.collect()\n",
    "        if data_props[\"has_timestamp\"]:\n",
    "            print('cleaning')\n",
    "            data = self.cleaning(data)\n",
    "            gc.collect()\n",
    "        data = self.reduce_memory_usage(data)\n",
    "        return data"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PLEASE ENTER A VALID DATASET NAME, VALID NAMES ARE :  test_series_1\n",
      "cleaning\n",
      "Number of missing timestamps :  0\n",
      "Percentage of removed rows : 0.0%\n",
      "Memory usage of dataframe is 2.67 MB\n",
      "Memory usage after optimization is: 4.02 MB\n",
      "Decreased by -50.5%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series_id</th>\n",
       "      <th>step</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>anglez</th>\n",
       "      <th>enmo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-05-03T10:30:00-0400</td>\n",
       "      <td>3.271484</td>\n",
       "      <td>0.038605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-05-03T10:30:05-0400</td>\n",
       "      <td>0.073120</td>\n",
       "      <td>0.066284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2018-05-03T10:30:10-0400</td>\n",
       "      <td>-2.457031</td>\n",
       "      <td>0.040802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2018-05-03T10:30:15-0400</td>\n",
       "      <td>-5.468750</td>\n",
       "      <td>0.081970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2018-05-03T10:30:20-0400</td>\n",
       "      <td>-10.007812</td>\n",
       "      <td>0.078796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>99995.0</td>\n",
       "      <td>2018-05-09T05:22:55-0400</td>\n",
       "      <td>7.707031</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>99996.0</td>\n",
       "      <td>2018-05-09T05:23:00-0400</td>\n",
       "      <td>7.707031</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>99997.0</td>\n",
       "      <td>2018-05-09T05:23:05-0400</td>\n",
       "      <td>7.707031</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>99998.0</td>\n",
       "      <td>2018-05-09T05:23:10-0400</td>\n",
       "      <td>7.707031</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>99999.0</td>\n",
       "      <td>2018-05-09T05:23:15-0400</td>\n",
       "      <td>7.707031</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          series_id     step                 timestamp     anglez      enmo\n",
       "0      0a96f4993bd7      0.0  2018-05-03T10:30:00-0400   3.271484  0.038605\n",
       "1      0a96f4993bd7      1.0  2018-05-03T10:30:05-0400   0.073120  0.066284\n",
       "2      0a96f4993bd7      2.0  2018-05-03T10:30:10-0400  -2.457031  0.040802\n",
       "3      0a96f4993bd7      3.0  2018-05-03T10:30:15-0400  -5.468750  0.081970\n",
       "4      0a96f4993bd7      4.0  2018-05-03T10:30:20-0400 -10.007812  0.078796\n",
       "...             ...      ...                       ...        ...       ...\n",
       "99995  0a96f4993bd7  99995.0  2018-05-09T05:22:55-0400   7.707031  0.000000\n",
       "99996  0a96f4993bd7  99996.0  2018-05-09T05:23:00-0400   7.707031  0.000000\n",
       "99997  0a96f4993bd7  99997.0  2018-05-09T05:23:05-0400   7.707031  0.000000\n",
       "99998  0a96f4993bd7  99998.0  2018-05-09T05:23:10-0400   7.707031  0.000000\n",
       "99999  0a96f4993bd7  99999.0  2018-05-09T05:23:15-0400   7.707031  0.000000\n",
       "\n",
       "[100000 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reader = data_reader(demo_mode=False)\n",
    "test_series = reader.load_data(data_name=\"test_series_1\")\n",
    "ids = test_series.series_id.unique()\n",
    "gc.collect()\n",
    "test_series"
   ]
  },
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   "source": [
    "class ResidualBiGRU(nn.Module):\n",
    "    def __init__(self, hidden_size, n_layers=1, bidir=True):\n",
    "        super(ResidualBiGRU, self).__init__()\n",
    "\n",
    "        self.hidden_size = hidden_size\n",
    "        self.n_layers = n_layers\n",
    "\n",
    "        self.gru = nn.GRU(\n",
    "            hidden_size,\n",
    "            hidden_size,\n",
    "            n_layers,\n",
    "            batch_first=True,\n",
    "            bidirectional=bidir,\n",
    "        )\n",
    "        dir_factor = 2 if bidir else 1\n",
    "        self.fc1 = nn.Linear(\n",
    "            hidden_size * dir_factor, hidden_size * dir_factor * 2\n",
    "        )\n",
    "        self.ln1 = nn.LayerNorm(hidden_size * dir_factor * 2)\n",
    "        self.fc2 = nn.Linear(hidden_size * dir_factor * 2, hidden_size)\n",
    "        self.ln2 = nn.LayerNorm(hidden_size)\n",
    "\n",
    "    def forward(self, x, h=None):\n",
    "        res, new_h = self.gru(x, h)\n",
    "        # res.shape = (batch_size, sequence_size, 2*hidden_size)\n",
    "\n",
    "        res = self.fc1(res)\n",
    "        res = self.ln1(res)\n",
    "        res = nn.functional.relu(res)\n",
    "\n",
    "        res = self.fc2(res)\n",
    "        res = self.ln2(res)\n",
    "        res = nn.functional.relu(res)\n",
    "\n",
    "        # skip connection\n",
    "        res = res + x\n",
    "\n",
    "        return res, new_h\n",
    "\n",
    "class MultiResidualBiGRU(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, out_size, n_layers, bidir=True):\n",
    "        super(MultiResidualBiGRU, self).__init__()\n",
    "\n",
    "        self.input_size = input_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.out_size = out_size\n",
    "        self.n_layers = n_layers\n",
    "\n",
    "        self.fc_in = nn.Linear(input_size, hidden_size)\n",
    "        self.ln = nn.LayerNorm(hidden_size)\n",
    "        self.res_bigrus = nn.ModuleList(\n",
    "            [\n",
    "                ResidualBiGRU(hidden_size, n_layers=1, bidir=bidir)\n",
    "                for _ in range(n_layers)\n",
    "            ]\n",
    "        )\n",
    "        self.fc_out = nn.Linear(hidden_size, out_size)\n",
    "\n",
    "    def forward(self, x, h=None):\n",
    "        # if we are at the beginning of a sequence (no hidden state)\n",
    "        if h is None:\n",
    "            # (re)initialize the hidden state\n",
    "            h = [None for _ in range(self.n_layers)]\n",
    "\n",
    "        x = self.fc_in(x)\n",
    "        x = self.ln(x)\n",
    "        x = nn.functional.relu(x)\n",
    "\n",
    "        new_h = []\n",
    "        for i, res_bigru in enumerate(self.res_bigrus):\n",
    "            x, new_hi = res_bigru(x, h[i])\n",
    "            new_h.append(new_hi)\n",
    "\n",
    "        x = self.fc_out(x)\n",
    "        return x, new_h  # log probabilities + hidden states"
   ]
  },
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 61.95it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class SleepDataset(Dataset):\n",
    "    def __init__(\n",
    "        self,\n",
    "        series_ids,\n",
    "        series,\n",
    "    ):\n",
    "        series_ids = series_ids\n",
    "        series = series.reset_index()\n",
    "        self.data = []\n",
    "        \n",
    "        for viz_id in tqdm(series_ids):\n",
    "            self.data.append(series.loc[(series.series_id==viz_id)].copy().reset_index())\n",
    "            \n",
    "    def downsample_seq_generate_features(self,feat, downsample_factor):\n",
    "        \n",
    "        if len(feat)%12!=0:\n",
    "            feat = np.concatenate([feat,np.zeros(12-((len(feat))%12))+feat[-1]])\n",
    "        feat = np.reshape(feat, (-1,12))\n",
    "        feat_mean = np.mean(feat,1)\n",
    "        feat_std = np.std(feat,1)\n",
    "        feat_median = np.median(feat,1)\n",
    "        feat_max = np.max(feat,1)\n",
    "        feat_min = np.min(feat,1)\n",
    "\n",
    "        return np.dstack([feat_mean,feat_std,feat_median,feat_max,feat_min])[0]\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        X = self.data[index][['anglez','enmo']].values.astype(np.float32)\n",
    "        X = np.concatenate([self.downsample_seq_generate_features(X[:,i],12) for i in range(X.shape[1])],-1)\n",
    "        X = torch.from_numpy(X)\n",
    "        return X\n",
    "test_ds = SleepDataset(test_series.series_id.unique(),test_series)\n",
    "del test_series\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6427b3dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-18T23:14:09.023356Z",
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     "end_time": "2023-10-18T23:14:09.029435",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "max_chunk_size = 24*60*100\n",
    "min_interval = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d1dd785",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-18T23:14:09.035610Z",
     "iopub.status.busy": "2023-10-18T23:14:09.035306Z",
     "iopub.status.idle": "2023-10-18T23:14:10.656048Z",
     "shell.execute_reply": "2023-10-18T23:14:10.654511Z"
    },
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     "duration": 1.62678,
     "end_time": "2023-10-18T23:14:10.658889",
     "exception": false,
     "start_time": "2023-10-18T23:14:09.032109",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = MultiResidualBiGRU(input_size=10,hidden_size=64,out_size=2,n_layers=5).to(device).eval()\n",
    "model.load_state_dict(torch.load(f'./model_best.pth',map_location=device))\n",
    "submission = pd.DataFrame()\n",
    "for i in range(len(test_ds)):\n",
    "    X = test_ds[i].half()\n",
    "    \n",
    "    seq_len = X.shape[0]\n",
    "    h = None\n",
    "    pred = torch.zeros((len(X),2)).half()\n",
    "    for j in range(0, seq_len, max_chunk_size):\n",
    "        y_pred, h = model(X[j: j + max_chunk_size].float(), h)\n",
    "        h = [hi.detach() for hi in h]\n",
    "        pred[j : j + max_chunk_size] = y_pred.detach()\n",
    "        del y_pred;gc.collect()\n",
    "    del h,X;gc.collect()\n",
    "    pred = pred.numpy()\n",
    "    \n",
    "    series_id = ids[i]\n",
    "    \n",
    "    days = len(pred)/(17280/12)\n",
    "    scores0,scores1 = np.zeros(len(pred),dtype=np.float16),np.zeros(len(pred),dtype=np.float16)\n",
    "    for index in range(len(pred)):\n",
    "        if pred[index,0]==max(pred[max(0,index-min_interval):index+min_interval,0]):\n",
    "            scores0[index] = max(pred[max(0,index-min_interval):index+min_interval,0])\n",
    "        if pred[index,1]==max(pred[max(0,index-min_interval):index+min_interval,1]):\n",
    "            scores1[index] = max(pred[max(0,index-min_interval):index+min_interval,1])\n",
    "    candidates_onset = np.argsort(scores0)[-max(1,round(days)):]\n",
    "    candidates_wakeup = np.argsort(scores1)[-max(1,round(days)):]\n",
    "    \n",
    "    onset = test_ds.data[i][['step']].iloc[np.clip(candidates_onset*12,0,len(test_ds.data[i])-1)].astype(np.int32)\n",
    "    onset['timestamp'] = test_ds.data[i][['timestamp']].iloc[np.clip(candidates_onset*12,0,len(test_ds.data[i])-1)]\n",
    "    onset['event'] = 'onset'\n",
    "    onset['series_id'] = series_id\n",
    "    onset['score']= scores0[candidates_onset]\n",
    "    wakeup = test_ds.data[i][['step']].iloc[np.clip(candidates_wakeup*12,0,len(test_ds.data[i])-1)].astype(np.int32)\n",
    "    wakeup['timestamp'] = test_ds.data[i][['timestamp']].iloc[np.clip(candidates_wakeup*12,0,len(test_ds.data[i])-1)]\n",
    "    wakeup['event'] = 'wakeup'\n",
    "    wakeup['series_id'] = series_id\n",
    "    wakeup['score']= scores1[candidates_wakeup]\n",
    "    submission = pd.concat([submission,onset,wakeup],axis=0)\n",
    "    del onset,wakeup,candidates_onset,candidates_wakeup,scores0,scores1,pred,series_id,\n",
    "    gc.collect()\n",
    "submission = submission.sort_values(['series_id','step']).reset_index(drop=True)\n",
    "submission['row_id'] = submission.index.astype(int)\n",
    "submission['score'] = submission['score'].fillna(submission['score'].mean())\n",
    "submission = submission[['row_id','series_id','step','event','score', 'timestamp']]\n",
    "submission.to_csv('submission.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "907776ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-18T23:14:10.666141Z",
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     "shell.execute_reply": "2023-10-18T23:14:10.677328Z"
    },
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     "duration": 0.017502,
     "end_time": "2023-10-18T23:14:10.679758",
     "exception": false,
     "start_time": "2023-10-18T23:14:10.662256",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>series_id</th>\n",
       "      <th>step</th>\n",
       "      <th>event</th>\n",
       "      <th>score</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>8148</td>\n",
       "      <td>onset</td>\n",
       "      <td>7.359375</td>\n",
       "      <td>2018-05-03T21:49:00-0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>14928</td>\n",
       "      <td>wakeup</td>\n",
       "      <td>8.507812</td>\n",
       "      <td>2018-05-04T07:14:00-0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>26004</td>\n",
       "      <td>onset</td>\n",
       "      <td>9.007812</td>\n",
       "      <td>2018-05-04T22:37:00-0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>33864</td>\n",
       "      <td>wakeup</td>\n",
       "      <td>8.664062</td>\n",
       "      <td>2018-05-05T09:32:00-0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>45024</td>\n",
       "      <td>onset</td>\n",
       "      <td>8.117188</td>\n",
       "      <td>2018-05-06T01:02:00-0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>51012</td>\n",
       "      <td>wakeup</td>\n",
       "      <td>4.738281</td>\n",
       "      <td>2018-05-06T09:21:00-0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>60816</td>\n",
       "      <td>onset</td>\n",
       "      <td>9.148438</td>\n",
       "      <td>2018-05-06T22:58:00-0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>67128</td>\n",
       "      <td>wakeup</td>\n",
       "      <td>9.132812</td>\n",
       "      <td>2018-05-07T07:44:00-0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>77052</td>\n",
       "      <td>onset</td>\n",
       "      <td>9.289062</td>\n",
       "      <td>2018-05-07T21:31:00-0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>83940</td>\n",
       "      <td>wakeup</td>\n",
       "      <td>9.242188</td>\n",
       "      <td>2018-05-08T07:05:00-0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>94680</td>\n",
       "      <td>onset</td>\n",
       "      <td>9.460938</td>\n",
       "      <td>2018-05-08T22:00:00-0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0a96f4993bd7</td>\n",
       "      <td>99996</td>\n",
       "      <td>wakeup</td>\n",
       "      <td>3.791016</td>\n",
       "      <td>2018-05-09T05:23:00-0400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    row_id     series_id   step   event     score                 timestamp\n",
       "0        0  0a96f4993bd7   8148   onset  7.359375  2018-05-03T21:49:00-0400\n",
       "1        1  0a96f4993bd7  14928  wakeup  8.507812  2018-05-04T07:14:00-0400\n",
       "2        2  0a96f4993bd7  26004   onset  9.007812  2018-05-04T22:37:00-0400\n",
       "3        3  0a96f4993bd7  33864  wakeup  8.664062  2018-05-05T09:32:00-0400\n",
       "4        4  0a96f4993bd7  45024   onset  8.117188  2018-05-06T01:02:00-0400\n",
       "5        5  0a96f4993bd7  51012  wakeup  4.738281  2018-05-06T09:21:00-0400\n",
       "6        6  0a96f4993bd7  60816   onset  9.148438  2018-05-06T22:58:00-0400\n",
       "7        7  0a96f4993bd7  67128  wakeup  9.132812  2018-05-07T07:44:00-0400\n",
       "8        8  0a96f4993bd7  77052   onset  9.289062  2018-05-07T21:31:00-0400\n",
       "9        9  0a96f4993bd7  83940  wakeup  9.242188  2018-05-08T07:05:00-0400\n",
       "10      10  0a96f4993bd7  94680   onset  9.460938  2018-05-08T22:00:00-0400\n",
       "11      11  0a96f4993bd7  99996  wakeup  3.791016  2018-05-09T05:23:00-0400"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c13d90",
   "metadata": {},
   "source": [
    "# 绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f2b8a4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "import gc\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from warnings import simplefilter\n",
    "simplefilter(action=\"ignore\", category=pd.errors.PerformanceWarning)\n",
    "def plot_by_freq(df, freq, col):\n",
    "    \n",
    "    serie_id = df['series_id'].unique()[0]\n",
    "    fig = go.Figure()\n",
    "    # set up trace for wakeup\n",
    "    df_wakeup = df[df['event']=='wakeup']\n",
    "    fig.add_traces(go.Ohlc(x=df_wakeup.index,\n",
    "                    open=df_wakeup[f'open_{col}'], high=df_wakeup[f'high_{col}'],\n",
    "                    low=df_wakeup[f'low_{col}'], close=df_wakeup[f'close_{col}'],\n",
    "                   increasing_line_color= 'orange', decreasing_line_color= 'orange', name='awake'\n",
    "                ))\n",
    "    # set up traces for onset\n",
    "    df_onset = df[df['event']=='onset']\n",
    "    fig.add_traces(go.Ohlc(x=df_onset.index,\n",
    "                    open=df_onset[f'open_{col}'], high=df_onset[f'high_{col}'],\n",
    "                    low=df_onset[f'low_{col}'], close=df_onset[f'close_{col}'],\n",
    "                   increasing_line_color= 'blue', decreasing_line_color= 'blue', name='sleeping'\n",
    "                ))\n",
    "    fig.update_layout(title=f'Serie_id: {serie_id} - {freq} OLHC {col}', xaxis_rangeslider_visible=True)\n",
    "    fig.show()\n",
    "    \n",
    "\n",
    "def plot_columns_by_freq(df, freq, columns):\n",
    "    for col in columns:\n",
    "        df_ohlc = get_df_by_freq(df, freq, col)\n",
    "        plot_by_freq(df_ohlc, freq, col)\n",
    "\n",
    "def load_serie_by_id(idx):\n",
    "    df = pd.read_parquet('./test_series_1.parquet', filters=[('series_id','=',idx)])\n",
    "    df['series_id'] = df['series_id'].astype('category')\n",
    "    df['timestamp'] = pd.to_datetime(df['timestamp']).apply(lambda t: t.tz_localize(None))\n",
    "    return df\n",
    "\n",
    "def get_df_by_freq(df, freq, col):\n",
    "    df = df.copy()\n",
    "    df.set_index('timestamp', inplace=True)\n",
    "    df_ohlc = df[col].resample(freq).ohlc()\n",
    "    df_ohlc.columns = [f'{c}_{col}' for c in df_ohlc.columns]\n",
    "    df_ohlc = pd.merge(df_ohlc, df[['event', 'series_id']], left_index=True, right_index=True)\n",
    "    return df_ohlc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "87944cb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "SIGMA = 720 # 12 * 60\n",
    "def gauss(n=SIGMA,sigma=SIGMA*0.15):\n",
    "    # guassian distribution function\n",
    "    r = range(-int(n/2),int(n/2)+1)\n",
    "    return [1 / (sigma * sqrt(2*pi)) * exp(-float(x)**2/(2*sigma**2)) for x in r]\n",
    "\n",
    "def get_macd(s, hfast=4, hslow=48, macd=15, center=False):\n",
    "    sma_fast = s.rolling(hfast*12*60, min_periods=1, center=center).agg('mean')\n",
    "    sma_slow = s.rolling(hslow*12*60, min_periods=1, center=center).agg('mean')\n",
    "    macd = (sma_fast - sma_slow).rolling(12*macd, min_periods=1, center=center).mean().astype(np.float32)\n",
    "    return macd\n",
    "\n",
    "def macd_feat(df, col='anglezdiffabs', dif=120):\n",
    "    s = df[f'{col}']\n",
    "    df[f'{col}_macd'] = get_macd(s)\n",
    "    s = df[f'{col}'].sort_index(ascending=False)\n",
    "    df[f'{col}_macd_rev'] = get_macd(s).sort_index()\n",
    "    df[f'{col}_spred'] = df[f'{col}_macd']-df[f'{col}_macd_rev']\n",
    "    df[f'{col}_spread_diff']=df[f'{col}_spred'].diff(dif)\n",
    "    return df\n",
    "\n",
    "\n",
    "def feat_eng(df):\n",
    "    df['series_id'] = df['series_id'].astype('category')\n",
    "    df['timestamp'] = pd.to_datetime(df['timestamp']).apply(lambda t: t.tz_localize(None))\n",
    "    df['hour'] = df[\"timestamp\"].dt.hour\n",
    "    df['month'] = df[\"timestamp\"].dt.month\n",
    "    df.sort_values(['timestamp'], inplace=True)\n",
    "    df.set_index('timestamp', inplace=True)\n",
    "    df['enmo'] = df['enmo'].clip(upper=4)\n",
    "    df['lids'] = np.maximum(0., df['enmo'] - 0.02)\n",
    "    df['lids'] = df['lids'].rolling(f'{120*5}s', center=True, min_periods=1).agg('sum')\n",
    "    df['lids'] = 100 / (df['lids'] + 1)\n",
    "    df['lids'] = df['lids'].rolling(f'{360*5}s', center=True, min_periods=1).agg('mean').astype(np.float32)\n",
    "    df['lids_sma_fast'] = df['lids'].rolling('2h', center=True, min_periods=1).agg('mean').astype(np.float32)\n",
    "    df['lids_sma_macd'] = (df['lids'].rolling('18h', center=True, min_periods=1).agg('mean') - df['lids_sma_fast']\n",
    "                                 ).rolling(f'1h', center=True, min_periods=1).agg('mean').astype(np.float32)\n",
    "    df[\"enmo\"] = (df[\"enmo\"]*1000).astype(np.int16)\n",
    "    df[\"anglez\"] = df[\"anglez\"].astype(np.int16)\n",
    "    df[\"anglezdiffabs\"] = df[\"anglez\"].diff().abs().astype(np.float32)\n",
    "    for col in ['enmo', 'anglez', 'anglezdiffabs']:\n",
    "        periods = [720] \n",
    "        for n in periods:\n",
    "            rol_args = {'window':f'{n+5}s', 'min_periods':10, 'center':True}\n",
    "            for agg in ['std', 'mean']:\n",
    "                df[f'{col}_{agg}_{n}'] = df[col].rolling(**rol_args).agg(agg).astype(np.float32).values\n",
    "                gc.collect()\n",
    "            rol_args = {'window':f'8h', 'min_periods':15, 'center':True}\n",
    "            df[f'{col}_std_max_8h'] = df[f'{col}_std_{n}'].rolling(**rol_args).max().astype(np.float32).values\n",
    "    df.reset_index(inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ea064cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_events = submission.drop(['row_id', 'score'], axis=1)\n",
    "test_events['timestamp'] = pd.to_datetime(test_events['timestamp']).apply(lambda t: t.tz_localize(None))\n",
    "count_na = test_events.groupby('series_id')['timestamp'].apply(lambda x: x.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c12c16ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID: 0a96f4993bd7\n",
      "========================================================================================================================\n"
     ]
    },
    {
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    "for idx in count_na[count_na>=0].index[:3]:\n",
    "    print(f'ID: {idx}', \"=\"*120, sep='\\n')\n",
    "    test_series = load_serie_by_id(idx)\n",
    "    df = test_series.merge(test_events[test_events['series_id']==idx], on=['series_id', 'timestamp'], how='left').ffill()\n",
    "    df = feat_eng(df)\n",
    "    plot_columns_by_freq(df, '5min', ['anglez', 'anglezdiffabs_mean_720', 'enmo', 'enmo_mean_720'])"
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